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Angle-resolved magneto-chiral anisotropy in a non-centrosymmetric atomic layer superlattice

Science Bulletin 2025年第9期70卷 1406-1409页

作者： Cheng, Long Bao, Mingrui Zhang, Xue Zhang, Jingxian Yang, Qun Li, Qiang Cao, Hui Qiu, Dawei Li, Hui Cheng, Guanglei Zhou, Hua Zuo, Jian-Min Zhou, Xiaodong Shen, Jian Zhu, Zhifeng Wang, Wenbo Zhai, Xiaofang School of Physical Science and Technology ShanghaiTech University Shanghai201210 China CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China Hefei230026 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China X-ray Science Division Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Department of Materials Science and Engineering and Materials Research Laboratory University of Illinois Urbana–Champaign UrbanaIL61801 United States ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China

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HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

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Computers in Biology and Medicine 2025年 190卷 109991-109991页

作者： Ahmad, Zahoor Al-Thelaya, Khaled Alzubaidi, Mahmood Joad, Faaiz Gilal, Nauman Ullah Mifsud, William Boughorbel, Sabri Pintore, Giovanni Gobbetti, Enrico Schneider, Jens Agus, Marco Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Kite Pharma Santa MonicaCA United States Visual and Data-intensive Computing CRS4 Italy Qatar Computing Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images. © 2025 The Authors

关键词： Image annotation

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AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1626-1638页

作者： Haboub, Amine Baali, Hamza Bouzerdoum, Abdesselam Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Ar Rayyan Qatar University of Adelaide School of Electrical and Mechanical Engineering Australia Hamad Bin Khalifa University Ar Rayyan Qatar

This paper proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multi-layer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on thirteen different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three. © 2020 IEEE.

关键词： Convolutional neural networks

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Expedited Noise Spectroscopy of Transmon Qubits

arXiv

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arXiv 2025年

作者： Gupta, Bhavesh Joshi, Vismay Kandpal, Udit Mandayam, Prabha Gheeraert, Nicolas Dhomkar, Siddharth Department of Physics Indian Institute of Technology Madras Chennai600036 India Center for Quantum Information Communication and Computing Indian Institute of Technology Madras Chennai600036 India Division of Sciences Krea University Sri City517646 India

There has been tremendous progress in the physical realization of quantum computing hardware in recent times, bringing us closer than ever before to realizing the promise of quantum computing. However, noise continues to pose a crucial challenge when it comes to scaling up present day quantum processors. While decoherence limits the qubits’ ability to store information for long periods in the presence of uncontrollable noise sources, the erroneous implementation of control methods for state preparation and measurements leads to faulty implementations of quantum circuits. Conventional noise spectroscopy protocols can characterize and model environmental noise but are usually resource intensive and lengthy. Moreover, the underlying noise can vary in nature over time, making noise profile extraction futile as this new information cannot be harnessed to improve quantum error correction or dynamical decoupling protocols. In this work, we address this challenge using a machine learning-based methodology to quickly extract noise spectra of multiple qubits and demonstrate a possible noise mitigation strategy. The procedure involves implementing undemanding dynamical decoupling sequences to record coherence decays of the investigated qubits and then predict the underlying noise spectra with the help of a convolution neural network pretrained on a synthetic dataset. While our protocol is virtually hardware-agnostic, we validate its effectiveness using superconducting qubits available on the IBM Quantum platform. We further use these rapidly obtained, yet accurate, noise spectra to design bespoke dynamic decoupling sequences and perform time-dependent noise spectroscopy. © 2025, CC BY.

关键词： Qubits

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Shaping Connectivity: Channel Modeling and Performance Analysis of Reconfigurable Intelligent Surfaces for IoV Visible Light Communication

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IEEE Transactions on Vehicular technology 2025年

作者： Eldeeb, Hossien B. Qaraqe, Marwa Muhaidat, Sami Ghrayeb, Ali Tavakkolnia, Iman Haas, Harald University of Cambridge Electrical Engineering Division Department of Engineering Cambridge United Kingdom Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering United Kingdom Qatar Foundation Doha Qatar Khalifa University Computer and Communication Engineering Abu Dhabi United Arab Emirates

This paper presents a promising solution for enhancing the reliability and data rate of Internet-of-vehicles (IoV) visible light communication (VLC) systems through the utilization of optical reconfigurable intelligent surfaces (RISs). The focus is on the infrastructure-to-vehicle (I2V) scenario, where streetlights serve as internet access points. The IoV-VLC system is modeled using an advanced non-sequential raytracing approach. A novel closed-form channel model expression is proposed, incorporating transceiver, RIS, and infrastructure parameters. The proposed model is then validated against broadband raytracing simulation results considering different combinations of system parameters, including, receiver aperture size, streetlight pole height, spacing between the poles, and RIS height. Moreover, based on the derived channel model, the error rate and data rate performances of the system are analyzed. The required numbers of RIS elements allocated to each vehicle's location, to achieve a target error rate and data rate values, are further derived. Finally, this paper demonstrates the impact of various transceiver, RIS, and infrastructure parameters on the system performance. The obtained results reveal that the proposed IoV-VLC system with optimal RIS deployment effectively addresses system outage issues and meets reliability and data speed requirements regardless of the vehicle's location. The results also shed light on the influence of various factors on system performance. Notably, the total number of required RIS elements is significantly affected by factors such as receiver aperture size, space between the poles, and transmit power budget. For instance, when considering the same power budget and error rate target, the required number of RIS elements for spacing distances of 20 m, 22 m, and 24 m are determined as 132, 244, and 428 elements, respectively. Conversely, the impact of pole height is found to be minimal as long as the space between the poles remains

关键词： Budget control

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Variational M-Partite Geometric Entanglement Algorithm

arXiv

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arXiv 2025年

作者： Azimi-Mousolou, Vahid Singh, Prashant Department of Physics and Astronomy Uppsala University Box 516 UppsalaSE-751 20 Sweden Department of Information Technology Division of Scientific Computing Uppsala University Box 337 UppsalaSE-751 05 Sweden

We propose a variational quantum algorithm to evaluate the M-partite geometric entanglement across arbitrary partitions of an N-qubit system into M parties. By constructing tailored variational ansatz circuits for both single- and multi-qubit parties, we optimize the overlap between a target quantum state and an M-partite variational separable state. This method provides a flexible and scalable approach for characterizing arbitrary M-partite entanglement in complex quantum systems of a given dimension. We assess the accuracy of the proposed method by reproducing known analytical results and demonstrate its capability to evaluate entanglement among M parties for any given conventional or unconventional partitions of one- and two-dimensional spin systems, both near and at a quantum critical point. Our results establish the versatility of the variational approach in capturing different types of entanglement in various quantum systems, surpassing the capabilities of existing methods. Our approach offers a powerful methodology for advancing research in quantum information science, condensed matter physics, and quantum field theory. Additionally, we discuss its advantages, highlighting its adaptability to diverse system architectures in the context of near-term quantum devices. © 2025, CC BY.

关键词： Quantum entanglement

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Optimizing Building Energy Management Leveraging Adaptive Edge computing for Enhanced Efficiency and Occupant Well-Being 15th

Optimizing Building Energy Management Leveraging Adaptive Ed...

引用

15th International Symposium on Ambient Intelligence, ISAmI 2024

作者： Márquez-Sánchez, Sergio Alonso-Rollán, Sergio Nahom, Hayla Erbad, Aiman Fernandez, Javier Hernandez BISITE Research Group University of Salamanca Calle Espejo s/n. Edificio Multiusos I+D+i Salamanca37007 Spain Salamanca37188 Spain Information and Computing Technology Division College of Science and Engineering Hamad Bin Khalifa University A 036-F LAS Ar-Rayyan34110 Qatar College of Engineering Qatar University Doha2713 Qatar Iberdrola Innovation Middle East Doha210177 Qatar

ISBN: (纸本)9783031831164

Amidst growing environmental concerns and the push for sustainability, the intricacies of energy management in diverse building settings demand innovative solutions. Traditional strategies fail to address the unique characteristics and needs of different building types and users leading to inefficiencies and a lack of optimization in energy use. This study presents a sophisticated building energy management system, assessed via pilot implementations, which utilizes a platform based on adaptive and intelligent edge computing. This comprehensive system integrates various elements such as smart meters, HVAC controls, electric vehicle charging stations, and smart plugs. Utilizing the ICT PSP framework, the methodology encompasses rigorous data collection and analysis, emphasizing usability, error rectification, and the detailed monitoring of impacts. Key findings reveal that tailored energy management solutions, augmented by real-time data and user-centric interfaces, significantly improve energy efficiency. The study also highlighted the impact of socio-economic factors and weather conditions on energy consumption, underscoring the necessity of incorporating these variables into energy management strategies. The integration of demand-side management (DSM) through energy retailers, distribution system operators (DSO), or other power grid stakeholders has yielded valuable insights into external factors affecting energy consumption patterns. Specifically, these insights pertain to electricity tariffs and consumer behavior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy utilization

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery

PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wendi Yang Zihang Jiang Shang Zhao S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

With the recent advancements in single-image-based 3D human pose and shape estimation (3DHPSE), there is a growing amount of works that can achieve good results on standard benchmarks but struggle to yield accurate hu-man mesh in extreme scenarios like occlusion. Previous works propose to leverage 2D poses to help 3D HPSE model improve performance under occlusion, but usually rely on manual design to integrate 2D poses and only aim for specific kinds of occlusion. In this paper, we present PostoMETRO (Pose token enhanced MEsh TRansfOrmer), which integrates 2D pose prior knowledge as tokens into transformers to improve model's performance under occlusion. Using a VQ- VAE-based pose tokenizer; we efficiently represent 2D poses as tokens andfeed them to transformers together with image tokens. Subsequently, these tokens are queried by vertex andJoint tokens to decode 3D coordinates of mesh vertices and human Joints. Our proposed 2D poses integration strategy is manual-design-free and suitable for various kinds of occlusion. Experiments on both standard and occlusion-specific benchmarks demonstrate the effectiveness of PostoMETRO. Code will be made available 1 1 https://***/PostoMETRO/PostoMETRO-Paper.

关键词： Solid modeling Computer vision Three-dimensional displays Shape Estimation Manuals Benchmark testing Transformers Robustness Standards

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